Driven by the availability of large-scale datasets, Human Pose Estimation (HPE) plays a critical role in numerous downstream tasks. However, mainstream benchmarks exhibit severe representation bias, predominantly featuring able-bodied individuals.
arXiv:2608.22341v1 Announce Type: cross Abstract: Lifting 3D hand poses from 2D monocular representations remains challenging due to the limited availability of large-scale, diverse 3D-annotated hand...
The key challenge in articulated 3D object generation from a single image is accurately predicting the underlying kinematic structure. Existing methods either infer kinematic parameters directly from a static image that lacks dynamic part-level kinematic relationships, or estimate parameters from visual dynamics generated from a single image, which is prone to accumulated errors of two steps.
Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prosthesis users, this requires capturing both natural...
arXiv:2601.13913v3 Announce Type: replace Abstract: We consider monocular 3D human pose estimation (HPE), where the goal is to predict 3D human skeletal joints from a single 2D image, typically via 2...
arXiv:2609.18406v1 Announce Type: new Abstract: Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prost...
arXiv:2607. 08725v1 Announce Type: cross Abstract: Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable.
The paper introduces PICO, an end-to-end trainable model for 6DoF surgical tool pose estimation that uses multi-task learning to predict segmentation, depth, and pose parameters. It incorporates two geometry-aware proxy tasks—a projection loss and a point-to-point loss—to enforce consistency in 2D and 3D spaces, improving accuracy and robustness. Evaluated on the SurgRIPE dataset, PICO achieves strong performance, ranking second in rotation accuracy and maintaining competitive translation results, especially under occlusion.
arXiv:2608.29928v1 Announce Type: new Abstract: State-of-the-art monocular body recovery methods predict mesh vertices and angles on the corresponding kinematic tree, but their outputs lack biomechan...
arXiv:2607. 13646v1 Announce Type: cross Abstract: Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios.
KAD-Net introduces a Kinematics-Aware Decoupled Learning Network for 3D hand pose estimation from a single depth image. It employs a Finger Topology Constraint module that uses local kinematic representations of three consecutive finger joints to better model distal joint relationships and handle occlusion. The architecture also decouples 2D joint localization from depth estimation in a hierarchical multitask framework, reducing feature interference and improving accuracy on benchmark datasets such as ICVL, NYU, and MSRA.
FAMOS is a feed‑forward model that predicts movable‑part segmentation and joint parameters from a sparse, unordered set of partial point clouds. It jointly reasons over multiple observations using a Multi‑state Articulation Transformer that alternates state‑wise and global attention, and introduces an observed articulation span objective to supervise motion ranges across inputs. A procedural data generator supplies self‑annotated assets for training, and experiments on PartNet‑Mobility, ACD, and ArtiCraft‑10K show consistent improvements over existing feed‑forward and optimization‑based baselines.
arXiv:2610.01758v1 Announce Type: new Abstract: Category-level object pose estimation (COPE), capable of generalizing to intra-class unknown objects, has become a core technique for robotic 3D scene...